% !TEX root = ArticoloRF.tex
%conclusions
All the implemented algorithms exhibit comparable results in terms of classification error on the available dataset after reaching a certain amount of trees in the forest and a certain depth for each tree. 
Concerning running time, the Extremely Randomized version of the classifier is certainly the fastest   and, despite its naive approach, still shows results that are as good as those obtained with more sophisticated algorithms. 
However, compared to the other implementations, it is not as robust in terms of classification error if the number of training records is decreased, giving a considerably higher classification error rate. 
Although complex algorithms like Fisher's LD, Gini Index and Information Gain show a great initial robustness, their execution time is longer because of their intrinsic complexity that cannot be reduced by any mean. 
On the contrary, the strength of the Extremely Randomized version can be simply improved by increasing the number of the trees in the forest making it a viable solution even to solve complex classification problems.